Amid global scientific and technological (hereinafter “sci-tech”) competition and China’s innovation-driven strategy, achieving high-quality sci-tech innovation (HQDSTI) is crucial for economic transformation but faces challenges such as resource mismatch, insufficient funding, and low commercialization efficiency. Using panel data from 35 major Chinese cities (2013–2022), this study distinguishes between public sci-tech finance (PSTF) and market sci-tech finance (MSTF) and employs benchmark regression, mediation, and threshold models to investigate their impacts on HQDSTI. Results show that: (1) Both PSTF and MSTF significantly promote HQDSTI, with stronger effects in coastal, dual-center, and pilot cities, and in regions with low fiscal decentralization. MSTF is more effective under high marketization, while PSTF and overall STF are more effective under high financial development. (2) Industrial upgrading serves as a positive mediator, whereas venture capital exerts a suppressive mediating effect that intensifies as its scale expands. The promoting effect of industrial upgrading weakens beyond the threshold level. (3) Policy recommendations include differentiated financial strategies: fostering market-oriented instruments in coastal cities, optimizing targeted support in inland areas, strengthening regional and public–market financial coordination, and improving mechanisms of industrial upgrading and venture capital. This study provides theoretical insights for enhancing the synergistic effect between sci-tech finance and high-quality innovation development. • Distinguish public and market sci-tech finance, explore synergistic effects and differential impacts. • Develop a multi-dimensional evaluation framework for assessing high-quality sci-tech innovation. • Examine heterogeneity across five analytical dimensions to uncover regional and structural variations. • Reveal intermediary roles of industrial upgrading and venture capital. • Identify threshold effects and define the effective range of sci-tech finance.
This research explores the application of data mining techniques, specifically XGBoost, to predict game pricing trends and optimize discount strategies within the digital gaming market. Game prices are influenced by various factors, including production costs, market demand, and promotional strategies. This study analyzes historical pricing data from multiple online stores to identify key pricing patterns and factors that influence price changes over time. The model developed in this study predicts game prices by incorporating features such as retail price, discount percentages, past price trends (lags), and other time-based features. The findings reveal that retail price and recent price trends (e.g., 7-day rolling averages) are the most influential features in predicting future prices. Additionally, discount strategies significantly impact game sales, with certain discount ranges showing higher effectiveness in driving consumer purchases. The model also demonstrates variability in prediction accuracy, particularly at higher price points, highlighting the challenges of capturing complex price fluctuations in a dynamic digital marketplace. The significance of this study extends to the Metaverse market, where pricing and the use of digital assets like non-fungible tokens (NFTs) play a critical role. The model's application could aid in optimizing pricing strategies within virtual economies, enhancing both the consumer experience and retailer profitability. Future work includes integrating additional features such as user reviews and exploring its application to Metaverse game platforms. The practical implications of this research are significant for online game retailers looking to leverage data-driven insights for more effective pricing and promotional strategies.
Technological developments and the impact of artificial intelligence (AI) are omnipresent themes and concerns of the present day. Much has been written on these topics but applications of quantitative models to understand the techno-social landscape have been much more limited. We propose a mathematical model that can help understand in a unified manner the patterns underlying technological development and also identify the different regimes in which the technological landscape evolves. First, we develop a model of innovation diffusion between different technologies, the growth of each reinforcing the development of the others. The model has a variable that quantifies the level of development (or innovation, discovery) potential for a given technology. The potential, or market capacity, increases via diffusion from related technologies, reflecting the fact that a technology does not develop in isolation. Hence, the growth of each technology is influenced by how developed its neighboring (related) technologies are. This allows us to reproduce long-term trends seen in computing technology and large language models (LLMs). We then present a three-dimensional system of supply, demand, and investment which shows oscillations (business cycles) emerging if investment is too high into a given technology, product, or market. We finally combine the two models through a common variable and show that if investment or diffusion is too high in the network context, chaotic boom-bust cycles can emerge. These quantitative considerations allow us to reproduce the boom-bust patterns seen in non-fungible token (NFT) transaction data and also have deep implications for the development of AI which we highlight, such as the arrival of a new AI winter.
ABSTRACT Despite the universal acknowledgment of financial profit expectations as an investment driver, environmental concern has been suggested as a factor influencing investors' decisions to purchase cryptocurrency. In this sense, this study investigates the impact of environmental information on investment allocation decisions to purchase different types of cryptocurrencies with different levels of environmental impacts (i.e., cryptocurrencies using Proof‐of‐Work (Bitcoin) and Proof‐of‐Stake (Ether) consensus algorithms). This study used an online survey involving 199 respondents in experimental groups (receiving environmental information before allocating decision) and control groups (receiving no environmental information before allocating decision) to split an imaginary fund into Bitcoin and Ether. No significant difference in allocating capital was found between the groups regardless of investment horizon, time of affiliation as a cryptocurrency investor, education level of the respondents, and perceived importance of environmental impacts. Possible explanations for this insensitivity are widespread prior knowledge about the environmental impact of Bitcoin, psychological reactance towards environmental information, and the assessed overall low perceived importance of environmental impact for investment decisions in cryptocurrencies. The lack of significant impact found in such an experimental study implies that environmental education alone cannot be sufficient to shift investor preferences. The findings offer initial insights into the impact of environmental awareness on cryptocurrency investment motivations and provide empirical evidence to understand cryptocurrency investment behaviors in the current research scene. The results suggest researchers and policymakers investigate further investors' motives while coming up with more restrictive policy instruments to mitigate the negative environmental impact of cryptocurrencies.
This study examines the nexus between Google Trends’ collective interest in specific keywords related to technological advancements utilized in design and the stock performance of major companies in design-related sectors. Specifically, the paper examines causality patterns between Google Trends keywords and stock prices of design companies, also employing multi-fractal detrended cross-correlation analysis, to test for long-term relationships. According to the results, varying impacts across keywords on stock prices are identified, with non-fungible token (NFT) exhibiting the greatest influence, followed by three-dimensional (3D) printing and computer-aided design, virtual reality (VR) displays a noteworthy impact, while artificial intelligence (AI) design and generative design indicate the least impact. The results also reveal persistent long-term relationships between the examined variables, with rich multifractal behavior indicating complex relationships, mostly balanced. The findings are important for policymakers and managers, necessitating close monitoring, especially of NFT, and for design companies to align strategies for market movements. • Examine how online interest in design technologies influences stock performance in design-focused industries. • Identify NFTs and 3D printing as major drivers of stock movements in the design market. • Reveal multifractal patterns indicating persistent, complex links between trend data and stock values. • Recommend tracking emerging design technologies for timely decision-making in investment and policy. • Provide data-driven insights for anticipating stock behavior in response to evolving digital interest.
Mark Ng, Monica Law, Brian Wong Chi Bo, Michael Liang
Purpose This study explores key factors influencing individuals' intentions to invest in NFTs, focusing on personal innovativeness, reward sensitivity, knowledge, subjective norms, perceived value and perceived risk. The aim is to provide insights into what motivates investors within this emerging market, addressing a gap in the understanding of NFT adoption from an investor perspective. Design/methodology/approach An online survey collected data from 272 participants in China and Hong Kong. The research employs partial least squares-structural equation modeling (PLS-SEM) to assess the relationships between various individual, social and market factors and NFT investment intentions. Findings The results suggest that personal innovativeness, reward sensitivity, NFT knowledge, subjective norms and perceived value positively impact NFT investment intentions. Additionally, age and income moderate the effects of subjective norms and perceived value on investment intentions, highlighting demographic influences. Practical implications For practitioners, insights into investor motivators can inform strategies to promote NFT investments, such as promoting the high reward potential, enhancing investor knowledge, leveraging social proof and emphasizing NFTs' perceived value. For academics, the findings open pathways for further research into investor psychology and the evolving dynamics of NFT and traditional investment markets. Originality/value This study advances NFT literature by identifying determinants of NFT investment behavior, a relatively uncharted area. By incorporating theories from investment behavior and technology adoption, it provides a new framework to understand the psychological and social drivers specific to NFT investments.
Abstract Market efficiency assumes that prices in financial markets are perfectly informative and, therefore, it is not possible to design trading strategies that outperform the market. The concept of efficiency has important implications for financial stability and, consequently, for financial policies. If asset returns exhibit persistent or anti-persistent behavior, then predictability based on past returns might be possible, which would be a clear violation of the weak form of efficiency. Many studies rely on the Hurst exponent to evaluate the level of memory of financial returns, and the purpose of this paper is to show that long memory or anti-persistence of financial returns is not incompatible with the random walk model or the efficient market hypothesis (EMH). The use of the Hurst exponent to demonstrate the inefficiency of financial markets using common estimators is troublesome, especially when applied to financial returns, since values of $$\hat{H} \ne 0.5$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mover> <mml:mi>H</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mo>≠</mml:mo> <mml:mn>0.5</mml:mn> </mml:mrow> </mml:math> are not evidence against the random walk model or the EMH. Moreover, the high variability of Hurst exponent estimates and their dependence on the chosen algorithm should motivate careful use of this tool. This study proposes a simple theoretical explanation and an extensive simulation study to show that $$\hat{H} \ne 0.5$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mover> <mml:mi>H</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mo>≠</mml:mo> <mml:mn>0.5</mml:mn> </mml:mrow> </mml:math> for financial returns is perfectly compatible with the random walk model. As a robustness check, both the traditional rescaled range and the wavelet lifting algorithms are used. Applications to real data are also discussed to show that the empirical values of the Hurst exponent are in the range suggested by the simulations, providing evidence that over-reliance on the Hurst exponent could lead to erroneous rejection of the random walk model. Specifically, the paper presents an application to the daily returns of stock market indices (DJIA and S&P 500) over a period of more than 30 years and cryptocurrencies (Bitcoin and Ethereum) over a period of more than 5 years.
Two MEV builders now produce nearly 80\% of Ethereum blocks. Block builders have the ability to reorder transactions on the blockchain in a way that can be harmful to participants. We estimate they would pay in the aggregate nearly \$14 million per month to ensure that they remained in the first quartile of the block. Sandwich attacks, in which a transaction is front-run, are frequent, averaging more than one per block. Gas fees on these transactions pay for nearly 15\% of the MEV payments to the validator. These attacks have especially large marginal effects and skew the distribution. Reforms such as gas fee priority or private transaction pools might be helpful.
Jun Chen, Asma-Qamaliah Abdul-Hamid, Suhaiza Zailani
Although the potential of the blockchain has been extensively recognized by scholars and practitioners across multiple fields, research on its adoption in the framework of the circular economy (CE) is still scarce. In this context, this study extends the technology acceptance model (TAM) by integrating the technology–organization–environment (TOE) framework to holistically understand how technological perception factors (perceived usefulness and perceived ease of use) interact with organizational and environmental factors in influencing the intention to adopt the blockchain in the CE within the context of the Chinese automotive supply chain. Based on survey data from 305 respondents from Chinese automotive companies, the proposed hybrid TOE-TAM conceptual model was validated. The results indicate that, except for the effects of the knowledge management capability on the perceived ease of use and regulatory support on blockchain adoption intention, all of the other hypotheses are deemed significant. Moreover, by conducting an in-depth analysis of the evolution of blockchain adoption intention in the CE, this study not only deepens the understanding of how the technology is disseminated but also provides valuable insights to theory and practice within the Chinese automotive value chain.
George Bogdan Drăgan, Wissal Ben Arfi, Victor Tiberius, Aymen Ammari · 5 authors
This study fills a void in the literature on the intention to invest in sustainable cryptocurrency. It examines the behavioral antecedents of individuals' decision making in this expanding financial industry. A mixed-methods approach that combines partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) is used to gain an understanding of the factors that lead to acceptance of sustainable cryptocurrencies and the obstacles to investing in them. This study is valuable for investors, policymakers, and managers in the digital currency business, providing practical insights and strategic implications. The study's findings underscore the relevance of regulatory frameworks and government assistance in fostering the adoption of sustainable cryptocurrencies. The study emphasizes the importance of customer trust and sustainability in influencing the adoption of sustainable cryptocurrencies. It advances the theory of sustainable finance, technology adoption, and behavioral economics. The study's limitations and recommendations for future research offer a path to further the understanding of this developing topic. • This study uses PLS-SEM and fsQCA to analyze factors influencing sustainable cryptocurrency investments comprehensively. • PU, PEU, and PT significantly predict attitudes and behavioral intentions toward investing in sustainable cryptocurrencies. • The study shows ECS and SOS shape positive attitudes, while ENVS needs more awareness to become a key driver for investors. • The findings guide policymakers to improve regulations, highlight sustainable crypto benefits, and address concerns for wider adoption.
Khaladdin Rzayev, Αθανάσιος Σάκκας, Andrew Urquhart
The network effect, measured by users’ adoption, is considered an important driver of cryptocurrency market dynamics. This study examines the role of adoption timing in cryptocurrency markets by decomposing total adoption into two components: innovators (early adopters) and imitators (late adopters). We find that the innovators’ component is the primary driver of the association between user adoption and cryptocurrency returns, both in-sample and out-of-sample. Next, we show that innovators’ adoption improves price efficiency, while imitators’ adoption contributes to noisier prices. Furthermore, we demonstrate that the adoption model captures significant cryptocurrency market phenomena, such as herding behaviour, more effectively, making it better suited for forecasting models in cryptocurrency pricing. These results suggest that our methodology for linking early and late adopters to market dynamics can be applied to various domains, offering a framework for future research at the intersection of operational research and financial markets.
In recent years, propelled by societal transformations and technological advancements, emerging technologies founded upon diverse disciplines such as financial and information technology have rapidly evolved. Identifying the trends associated with these emerging technologies and extracting their salient topics is crucial in order to accurately grasp the developmental trajectory of these tools and for their efficient utilization. In this study, we chronologically categorize information derived from five types of multi-source data, including journal articles, patent inventions, and industry reports, into distinct periods. We employ the LDA (Latent Dirichlet Allocation) topic model to identify emerging technological themes within these periods and utilize a dual-index theme lifecycle analysis method to construct a hotspot theme distribution map, thereby facilitating the extraction of significant themes. Through empirical research on blockchain financial technology, we ultimately identify 22 thematic areas of blockchain finance and extracted eight prominent themes, including financial technology, cross-border payments, digital invoices, supply chain finance, and decentralization. By analyzing these themes alongside their respective popularity levels, we validate that the methods above can be used to effectively identify emerging technological hotspots and illuminate their developmental directions.
Zhang Ying, M. Mahdi Tavalaei, Glenn Parry, Peng Zhou
To understand the slow adoption of blockchain technology by organisations, we conduct a systematic literature review of adoption factors using a mixed-methods approach. Using thematic analysis, 880 factors are identified and grouped into 29 themes, which offer a comprehensive overview of the literature. Using statistical analysis, the identified factors are dissected into technological (T), organisational (O), and environmental (E) dimensions (the TOE framework). Themes are further classified as barriers (B), enablers (En), and ambiguous (A) to describe a firm's readiness for blockchain adoption (the BEnA framework). We emphasise the multidimensionality of adoption factors across the TOE dimensions and the conditionality of adoption enablers across the BEnA dimensions. Analysis of research trends shows that recent blockchain adoption literature has focused on elaborating upon existing research themes (involution) rather than on developing new themes (evolution). Based on our analyses, we propose future research directions, including scrutinising the interdependence and multidimensionality of blockchain adoption factors, further examining factors with conditional or unclear effects on adoption, and broadening the contextual, temporal, and theoretical aspects of blockchain adoption research. • Identified 880 factors and 29 themes of blockchain adoption by organisations. • Developed multidimensional theoretical frameworks to analyse factors and themes. • Adoption barriers are mostly unambiguous, while enablers are often conditional. • Recent research on blockchain is mainly an involution rather than an evolution.
This study explores the adoption of blockchain technology within the Ethiopian National Quality Infrastructure (NQI). Data from 178 professionals representing various organizations and roles were collected to investigate key adoption factors. By integrating the Technology Acceptance Model (TAM) and the Technology-Organization-Environment (TOE) framework, external factors (Technology Compatibility, Relative Advantage, Government Support, and Policy) and internal factors (perceived usefulness, perceived ease of use, and intention to use) were examined. A dual-stage analytical approach involving partial least square-based structural equation model (PLS-SEM) and artificial neural network (ANN) analyses was employed. The findings emphasize the significance of technological compatibility, perceived usefulness, and top management support as determinants of blockchain adoption in the NQI. Particularly, the compatibility of the existing system emerges as the most influential factor in adopting blockchain technology within the Ethiopian NQI. This study enhances the understanding of blockchain adoption within the NQI context, providing valuable insights for successful implementation. It contributes to the existing knowledge in this area and offers practical implications for quality infrastructure management.
Business models of industries and ventures enabled by new and emerging technologies are under-developed and fragmented. The blockchain technology promises to disrupt the business models. However, the scholarly literature in blockchain research is only beginning to emerge, and the majority are focused on the technical aspect leaving aside the business model complexities for blockchain applications. This paper fills this gap and proposes a blockchain business model framework. The research utilizes a sequential exploratory research design (SERD), encompassing two key phases: an initial phase involving a literature review and interviews to delineate blockchain business model building blocks and types, followed by a subsequent survey to identify the most pertinent building blocks and types. The findings helped to formulate the framework, necessary building blocks, and block types specifically for the blockchain applications. Organizations can tailor their business model through various configuration options and mechanisms, considering the arrangement of block types within each block according to their significant score levels. By giving priority to those with scores above the block's average, organizations can evaluate the performance of their models and pinpoint areas for enhancement. The proposed framework can be considered as a reference point for developing business models for blockchain applications. • Enhances current knowledge by classifying specific building blocks and types within the blockchain business model. • Outlines the blockchain business model, highlighting possible building blocks and types. • Creates an instrument to configure and measure the blockchain business model. • Determines the significance of different building blocks and types to aid decision-making in providing blockchain solutions.
Abstract Cryptocurrencies are a new form of digital assets that have gained increasing popularity in recent years. Investors have a dual objective of maximizing profits while minimizing risks. In today's world, there is an increase in the demand for cryptocurrencies, with focus on the emotional aspects as well as on the underlying technical analysis. This abstract provides a synthesis of recent research and insights into the behavior of consumers engaging with cryptocurrencies. Key determinants such as trust, perceived usefulness, and ease of use play pivotal roles in driving consumer adoption of cryptocurrencies. Furthermore, behavioral uncertainty and risk perception emerge as critical considerations impacting investment decisions within this dynamic ecosystem. The abstract also highlights the significant influence of digital platforms and social media on shaping consumer attitudes and behaviors towards cryptocurrencies, underscoring the importance of online discourse and information dissemination in this context. As the cryptocurrency market develops and grows, understanding consumer behavior becomes increasingly paramount for stakeholders, policymakers, and researchers alike. By unraveling the complexities of consumer preferences, motivations, and perceptions, this abstract offers valuable perspectives to inform strategic decision-making and foster sustainable growth in the cryptocurrency industry. This research was based on scientific articles and carefully selected and studied important data from trusted sources like academic journals, financial databases, and websites focusing on cryptocurrency information.
Elona Marku, Maria Chiara Di Guardo, Gerardo Patriotta, David G. Allen
Drawing on complexity theory, we investigate the structuring processes and underlying mechanisms underpinning the emergence of a new technology. Empirically, we track the emergence of blockchain technology by examining international patents issued between 2009 and 2020. Our results indicate that technology emergence follows an evolutionary trajectory that progresses from disordered to structured interactions among the technological elements, culminating in the formation of a technological core that acts as a pole of attraction for further interactions and delineates boundaries within the technological domain. Technology structuring is fueled by what we term “technology fitness” and “self-reinforcing” mechanisms that progressively transform primitive structures into more complex, self-organized configurations. Our study offers a novel framework of technology emergence, highlighting how dispersed bits of technological knowledge gradually aggregate into complex structures that define the specific trajectory of a particular domain.
Mark P. Doblas, Jishanis Mae G. Becaro, Jayendira P. Sankar, V. Natarajan · 6 authors
This study explores the adoption of cryptocurrency, specifically Bitcoin, in the Philippines. The authors argue that current behavioral prediction models, such as TRA, TPB, and TAM, do not adequately account for the affective constructs of decision-making when high monetary stakes are involved. To address this gap, the authors propose an integrative model that accounts for financial decision-making processes, risk constructs, and population-specific behavioral strategies. The study used a quantitative-based research design and involved 684 university students from one of the major state universities in the Philippines. The findings show that perceived usefulness, attitude toward cryptocurrency, self-efficacy, and descriptive norms significantly influenced the intention to adopt cryptocurrency. Overall, the study confirms the direct influence of instrumental attitude, knowledge of cryptocurrency, descriptive norm, risk tolerance, ease and difficulty, and control on an individual’s intention to use cryptocurrency. The study contributes to exploring a less studied Philippine consumer base and provides empirical findings and insights into Bitcoin adoption in developing Asian economies. JEL Classification: G15. G17, G41, P33, G32.
The results indicate a dynamic pattern of interconnectedness throughout history. Based on the findings, the transmission of volatility exhibited a higher magnitude during the period of COVID-19. The issue of high transmission volatility due to limited diversification options concerns investors, green stakeholders, and policymakers alike. This article proposes various potential areas for future research. The ICEA index can potentially assist businesses operating in environmentally sensitive sectors make well-informed policy decisions. It includes sectors such as environmental green bonds, and commodities. Consideration should be given to implementing blockchain technology, as it can consume less power in this particular scenario. By employing a time-frequency paradigm, this study is able to incorporate the investment horizon, a crucial factor to be taken into account when making financial judgments. The advancement of this research could be facilitated by directing our attention toward the implications of our findings on portfolios and developing appropriate measures for their evaluation.
This article investigates the Ethereum Merge, which occurred on 15 September 2022, and we employ the time-series difference in differences (DiD) model and vector autoregression (VAR) models and analyse how the protocol change from proof-of-work to proof-of-stake (PoS) affects the dynamic relationship between cryptocurrency returns and network factors. The results show that the Merge caused a structural change between Ethereum and Bitcoin networks. The network factors of Ethereum show a significant increase compared to Bitcoin, the cointegration has been strengthened and the lag length is shortened after the Merge. The spillover effect on the Bitcoin network can be seen from both DiD and VAR, indicating the increasing impact of the Ethereum network on Bitcoin. The concern of losing the number of participants due to the implantation of PoS on cryptocurrency is not apparent on Ethereum Merge, and it increases the investors’ attention and involvement.